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• ffmpeg realtime bad quality variable fps cams inputs to constant framerate problem

  23 janvier 2023, par BloodMan

      ../ffmpeg/ffmpeg -err_detect ignore_err -nostdin -threads 0 -y -strict experimental -thread_queue_size 10M -max_delay 20M -rtbufsize 20M -fflags +discardcorrupt \
        -i "${cam1}" -i "${cam2}" -i "${cam3}" -i "${cam4}" \
        -filter_complex " \
        nullsrc=size=3840x2160:rate=30 [main1]; \
        anullsrc=channel_layout=stereo:sample_rate=44100 [a]; \
        [0:v] scale=1920:1080 [overlay1]; \
        [1:v] scale=1920:1080 [overlay2]; \
        [2:v] scale=1920:1080 [overlay3]; \
        [3:v] scale=1920:1080 [overlay4]; \
        [main1][overlay1] overlay=0:0 [main2]; \
        [main2][overlay2] overlay=1920:0 [main3]; \
        [main3][overlay3] overlay=0:1080 [main4]; \
        [main4][overlay4] overlay=1920:1080 [v] " \
        -t 10 -r 30 -g 60 -map "[v]" -map "[a]" \
        -shortest -video_size 3840x2160 -pix_fmt yuv420p -vcodec libx264 -preset ultrafast -tune zerolatency -minrate 2M -maxrate 2M -bufsize 20M -c:a aac -b:a 96k -ac 2 -ar 48000 -copytb 1 \
        -f flv -y -fflags +genpts rtmp://b.rtmp.youtube.com/live2/${key}?backup=1


  


  ffmpeg version N-109650-g9d5e66942c Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 10 (Debian 10.2.1-6)
  configuration: --prefix=/home/bloodman/ffmpeg --pkg-config-flags=--static --extra-cflags='-I/home/bloodman/ffmpeg/include -march=native' --extra-ldflags=-L/home/bloodman/ffmpeg/lib --extra-libs='-lpthread -lm' --bindir=/home/bloodman/ffmpeg --enable-gpl --enable-nonfree --enable-libx264 --enable-libfdk-aac --enable-libmp3lame --enable-libfreetype --enable-hardcoded-tables
  libavutil      57. 44.100 / 57. 44.100
  libavcodec     59. 56.100 / 59. 56.100
  libavformat    59. 35.100 / 59. 35.100
  libavdevice    59.  8.101 / 59.  8.101
  libavfilter     8. 54.100 /  8. 54.100
  libswscale      6.  8.112 /  6.  8.112
  libswresample   4.  9.100 /  4.  9.100
  libpostproc    56.  7.100 / 56.  7.100
[hls @ 0x56019db77780] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019db77780] Opening 'cams/stream1_113.ts' for reading
Input #0, hls, from 'cams/stream1.m3u8':
  Duration: N/A, start: 1122.341667, bitrate: N/A
  Program 0
    Metadata:
      variant_bitrate : 0
  Stream #0:0: Video: h264 (Baseline) ([27][0][0][0] / 0x001B), yuv420p, 2048x1536, 15 fps, 15 tbr, 90k tbn
    Metadata:
      variant_bitrate : 0
[hls @ 0x56019db9e980] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019db9e980] Opening 'cams/stream2_105.ts' for reading
Input #1, hls, from 'cams/stream2.m3u8':
  Duration: N/A, start: 1042.633000, bitrate: N/A
  Program 0
    Metadata:
      variant_bitrate : 0
  Stream #1:0: Video: h264 (Main) ([27][0][0][0] / 0x001B), yuvj420p(pc, bt709), 1920x1080 [SAR 1:1 DAR 16:9], 25 fps, 100 tbr, 90k tbn
    Metadata:
      variant_bitrate : 0
[hls @ 0x56019dccdbc0] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019dccdbc0] Opening 'cams/stream3_14.ts' for reading
Input #2, hls, from 'cams/stream3.m3u8':
  Duration: N/A, start: 132.469000, bitrate: N/A
  Program 0
    Metadata:
      variant_bitrate : 0
  Stream #2:0: Video: h264 (Main) ([27][0][0][0] / 0x001B), yuv420p, 2688x1520, 25 fps, 100 tbr, 90k tbn
    Metadata:
      variant_bitrate : 0
[hls @ 0x56019f0ec980] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019f0ec980] Opening 'cams/stream4_26.ts' for reading
Input #3, hls, from 'cams/stream4.m3u8':
  Duration: N/A, start: 253.389000, bitrate: N/A
  Program 0
    Metadata:
      variant_bitrate : 0
  Stream #3:0: Video: h264 (High) ([27][0][0][0] / 0x001B), yuvj420p(pc, bt709), 1920x1080, 90k tbr, 90k tbn
    Metadata:
      variant_bitrate : 0
Stream mapping:
  Stream #0:0 (h264) -> scale:default
  Stream #1:0 (h264) -> scale:default
  Stream #2:0 (h264) -> scale:default
  Stream #3:0 (h264) -> scale:default
  overlay:default -> Stream #0:0 (libx264)
  anullsrc:default -> Stream #0:1 (aac)
[hls @ 0x56019db77780] Opening 'cams/stream1_114.ts' for reading
[hls @ 0x56019db77780] Opening 'cams/stream1_115.ts' for reading
[swscaler @ 0x5601a2c78e40] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x5601a332c940] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x5601a2c78e40] deprecated pixel format used, make sure you did set range correctly
    Last message repeated 2 times
[swscaler @ 0x5601a332c940] deprecated pixel format used, make sure you did set range correctly
[swscaler @ 0x5601a361fc00] deprecated pixel format used, make sure you did set range correctly
    Last message repeated 1 times
[libx264 @ 0x56019e5212c0] using SAR=1/1
[libx264 @ 0x56019e5212c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
[libx264 @ 0x56019e5212c0] profile Constrained Baseline, level 5.1, 4:2:0, 8-bit
[libx264 @ 0x56019e5212c0] 264 - core 160 r3011 cde9a93 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - http://www.videolan.org/x264.html - options: cabac=0 ref=1 deblock=0:0:0 analyse=0:0 me=dia subme=0 psy=1 psy_rd=1.00:0.00 mixed_ref=0 me_range=16 chroma_me=1 trellis=0 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=0 threads=8 lookahead_threads=8 sliced_threads=1 slices=8 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=60 keyint_min=6 scenecut=0 intra_refresh=0 rc_lookahead=0 rc=crf mbtree=0 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 vbv_maxrate=2000 vbv_bufsize=20000 crf_max=0.0 nal_hrd=none filler=0 ip_ratio=1.40 aq=0
Output #0, flv, to 'rtmp://b.rtmp.youtube.com/live2/XXXX-XXXX-XXXX-XXXX-XXXX?backup=1':
  Metadata:
    encoder         : Lavf59.35.100
  Stream #0:0: Video: h264 ([7][0][0][0] / 0x0007), yuv420p(progressive), 3840x2160 [SAR 1:1 DAR 16:9], q=2-31, 30 fps, 1k tbn
    Metadata:
      encoder         : Lavc59.56.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 2000000/0/0 buffer size: 20000000 vbv_delay: N/A
  Stream #0:1: Audio: aac (LC) ([10][0][0][0] / 0x000A), 48000 Hz, stereo, fltp, 96 kb/s
    Metadata:
      encoder         : Lavc59.56.100 aac
[hls @ 0x56019db77780] Skip ('#EXT-X-VERSION:3')00:00:03.22 bitrate=7519.6kbits/s speed=0.359x
[hls @ 0x56019db77780] Opening 'cams/stream1_116.ts' for reading
[hls @ 0x56019db77780] Skip ('#EXT-X-VERSION:3')00:00:07.23 bitrate=4470.2kbits/s speed=0.383x
[hls @ 0x56019db77780] Opening 'cams/stream1_117.ts' for reading
[hls @ 0x56019f0ec980] Skip ('#EXT-X-VERSION:3')00:00:09.04 bitrate=3978.1kbits/s speed=0.384x
[hls @ 0x56019f0ec980] Opening 'cams/stream4_27.ts' for reading
[hls @ 0x56019dccdbc0] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019dccdbc0] Opening 'cams/stream3_15.ts' for reading
[hls @ 0x56019db9e980] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019db9e980] Opening 'cams/stream2_106.ts' for reading
[flv @ 0x56019e639a00] Failed to update header with correct duration.811.6kbits/s speed=0.385x
[flv @ 0x56019e639a00] Failed to update header with correct filesize.
frame=  299 fps= 12 q=34.0 Lsize=    4622kB time=00:00:09.98 bitrate=3792.7kbits/s speed=0.386x
video:4603kB audio:3kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.349536%
[libx264 @ 0x56019e5212c0] frame I:5     Avg QP:45.60  size:275880
[libx264 @ 0x56019e5212c0] frame P:294   Avg QP:38.77  size: 11340
[libx264 @ 0x56019e5212c0] mb I  I16..4: 100.0%  0.0%  0.0%
[libx264 @ 0x56019e5212c0] mb P  I16..4:  1.4%  0.0%  0.0%  P16..4:  4.7%  0.0%  0.0%  0.0%  0.0%    skip:93.9%
[libx264 @ 0x56019e5212c0] coded y,uvDC,uvAC intra: 16.2% 8.9% 1.2% inter: 1.8% 0.6% 0.0%
[libx264 @ 0x56019e5212c0] i16 v,h,dc,p: 64% 17% 15%  5%
[libx264 @ 0x56019e5212c0] i8c dc,h,v,p: 90%  6%  3%  1%
[libx264 @ 0x56019e5212c0] kb/s:3783.23
[aac @ 0x56019e63a700] Qavg: 65511.207
[hls @ 0x56019db77780] Skip ('#EXT-X-VERSION:3')
[hls @ 0x56019db77780] Opening 'cams/stream1_118.ts' for reading


  


  NOTES : sources are cams streamed first to hls/m3u8. -t 10 only for testing purposes.

  


  The problem is variable output fps= 12 (sometimes 2, 5, 10, maybe 13) where I expect 30. Machine is 10 times greater (encode uses up to 5% cpu).

  


  Im trying adding -re, -r 30, -r 15 to sources, convert sources via stream_filter (,fps=30), vsync (old versions of ffmpeg), wallclock time, etc. and reading stackoverflow of course. And... nothing.

  


  Where is the problem ?

  


• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now. 

• How to Check Website Traffic As Accurately As Possible

  18 août 2023, par Erin — Analytics Tips

  If you want to learn about the health of your website and the success of your digital marketing initiatives, there are few better ways than checking your website traffic. 

  It’s a great way to get a quick dopamine hit when things are up, but you can also use traffic levels to identify issues, learn more about your users or benchmark your performance. That means you need a reliable and easy way to check your website traffic over time — as well as a way to check out your competitors’ traffic levels, too. 

  In this article, we’ll show you how to do just that. You’ll learn how to check website traffic for both your and your competitor’s sites and discover why some methods of checking website traffic are better than others. 

  Why check website traffic ? 

  Dopamine hits aside, it’s important to constantly monitor your website’s traffic for several reasons.

  

  Benchmark site performance

  Keeping regular tabs on your traffic levels is a great way to track your website’s performance over time. It can help you plan for the future or identify problems. 

  For instance, growing traffic levels may mean expanding your business’s offering or investing in more inventory. On the flip side, decreasing traffic levels may suggest it’s time to revamp your marketing strategies or look into issues impacting your SEO. 

  Analyse user behaviour

  Checking website traffic and user behaviour lets marketing managers understand how users interact with your website. Which pages are they visiting ? Which CTAs do they click on ? What can you do to encourage users to take the actions you want ? You can also identify issues that lead to high bounce rates and other problems. 

  The better you understand user behaviour, the easier it will be to give them what they want. For example, you may find that users spend more time on your landing pages than they do your blog pages. You could use that information to revise how you create blog posts or focus on creating more landing pages. 

  Improve the user experience

  Once you understand how users behave on your website, you can use that information to fix errors, update your content and improve the user experience for the site. 

  You can even personalise the experience for customers, leading to significant growth. Research shows companies that grow faster derive 40% more of their revenue from personalisation. 

  That could come in the form of sweeping personalisations — like rearranging your website’s navigation bar based on user behaviour — or individual personalisation that uses analytics to transform sections or entire pages of your site based on user behaviour. 

  Optimise marketing strategies

  You can use website traffic reports to understand where users are coming from and optimise your marketing plan accordingly. You may want to double down on organic traffic, for instance, or invest more in PPC advertising. Knowing current traffic estimates and how these traffic levels have trended over time can help you benchmark your campaigns and prioritise your efforts. 

  Increasing traffic levels from other countries can also help you identify new marketing opportunities. If you start seeing significant traffic levels from a neighbouring country or a large market, it could be time to take your business international and launch a cross-border campaign. 

  Filter unwanted traffic

  A not-insignificant portion of your site’s traffic may be coming from bots and other unwanted sources. These can compromise the quality of your analytics and make it harder to draw insights. You may not be able to get rid of this traffic, but you can use analytics tools to remove it from your stats. 

  How to check website traffic on Matomo

  If you want to check your website’s traffic, you’d be forgiven for heading to Google Analytics first. It’s the most popular analytics tool on the market, after all. But if you want a more reliable assessment of your website’s traffic, then we recommend using Matomo alongside Google Analytics. 

  The Matomo web analytics platform is an open-source solution that helps you collect accurate data about your website’s traffic and make more informed decisions as a result — all while enhancing the customer experience and ensuring GDPR compliance and user privacy. 

  Matomo also offers multiple ways to check website traffic :

  • Visits log report
  • Real-time visitor map
  • Visits in real-time report

  Let’s look at all of them one by one. 

  The visits log report is a unique rundown of all of the individual visitors to your site. This offers a much more granular view than other tools that just show the total number of visitors for a given period. 

  

  You can access the visits log report by clicking on the reporting menu, then clicking Visitor and Visits Log. From there, you’ll be able to scroll through every user session and see the following information :

  • The location of the user
  • The total number of actions they took
  • The length of time on site
  • How they arrived at your site
  • And the device they used to access your site 

  This may be overwhelming if your site receives thousands of visitors at a time. But it’s a great way to understand users at an individual level and appreciate the lifetime activity of specific users. 

  The Real-time visitor map is a visual display of users’ location for a given timeframe. If you have an international website, it’s a fantastic way to see exactly where in the world your traffic comes from.

  

  You can access the Real-time Visitor Map by clicking Visitor in the main navigation menu and then Real-time Map. The map itself is colour-coded. Larger orange bubbles represent recent visits, and smaller dark orange and grey bubbles represent older visits. The map will refresh every five seconds, and new users appear with a flashing effect. 

  If you run TV or radio adverts, Matomo’s Real-time Map provides an immediate read on the effectiveness of your campaign. If your map lights up in the minutes following your ad, you know it’s been effective. It can also help you identify the source of bot attacks, too. 

  Finally, the Visits in Real-time report provides a snapshot of who is browsing your website. You can access this report under Visitors > Real-time and add it to your custom dashboards as a widget. 

  Open the report, and you’ll see the real-time flow of your site’s users and counters for visits and pageviews over the last 30 minutes and 24 hours. The report refreshes every five seconds with new users added to the top of the report with a fade-in effect.

  

  The report provides a snapshot of each visitor, including :

  • Whether they are new or a returning 
  • Their country
  • Their browser
  • Their operating system
  • The number of actions they took
  • The time they spent on the site
  • The channel they came in from
  • Whether the visitor converted a goal

  3 other ways to check website traffic

  You don’t need to use Matomo to check your website traffic. Here are three other tools you can use instead. 

  How to check website traffic on Google Analytics

  Google Analytics is usually the first starting point for anyone looking to check their website traffic. It’s free to use, incredibly popular and offers a wide range of traffic reports. 

  Google Analytics lets you break down historical traffic data almost any way you wish. You can split traffic by acquisition channel (organic, social media, direct, etc.) by country, device or demographic.

  

  It also provides real-time traffic reports that give you a snapshot of users on your site right now and over the last 30 minutes. 

  

  Google Analytics may be one of the most popular ways to check website traffic, but it could be better. Google Analytics 4 is difficult to use compared to its predecessor, and it also limits the amount of data you can track in accordance with privacy laws. If users refuse your cookie consent, Google Analytics won’t record these visits. In other words, you aren’t getting a complete view of your traffic by using Google Analytics alone. 

  That’s why it’s important to use Google Analytics alongside other web analytics tools (like Matomo) that don’t suffer from the same privacy issues. That way, you can make sure you track every single user who visits your site. 

  How to check website traffic on Google Search Console

  Google Search Console is a free tool from Google that lets you analyse the search traffic that your site gets from Google. 

  The top-line report shows you how many times your website has appeared in Google Search, how many clicks it has received, the average clickthrough rate and the average position of your website in the search results. 

  

  Google Search Console is a great way to understand what you rank for and how much traffic your organic rankings generate. It will also show you which pages are indexed in Google and whether there are any crawling errors. 

  Unfortunately, Google Search Console is limited if you want to get a complete view of your traffic. While you can analyse search traffic in a huge amount of detail, it will not tell you how users who access your website directly or via social media behave. 

  How to check website traffic on Similarweb

  Similarweb is a website analysis tool that estimates the total traffic of any site on the internet. It is one of the best tools for estimating how much traffic your competitors receive. 

  What’s great about Similarweb is that it estimates total traffic, not just traffic from search engines like many SEO tools. It even breaks down traffic by different channels, allowing you to see how your website compares against your competitors. 

  

  As you can see from the image above, Similarweb provides an estimate of total visits, bounce rate, the average number of pages users view per visit and the average duration on the site. The company also has a free browser extension that lets you check website traffic estimates as you browse the web. 

  You can use Similarweb for free to a point. But to really get the most out of this tool, you’ll need to upgrade to a premium plan which starts at $125 per user per month. 

  The price isn’t the only downside of using Similarweb to check the traffic of your own and your competitor’s websites. Ultimately, Similarweb is only an estimate — even if it’s a reasonably accurate one — and it’s no match for a comprehensive analytics tool. 

  7 website traffic metrics to track

  Now that you know how to check your website’s traffic, you can start to analyse it. You can use plenty of metrics to assess the quality of your website traffic, but here are some of the most important metrics to track. 

  • New visitors : These are users who have never visited your website before. They are a great sign that your marketing efforts are working and your site is reaching more people. But it’s also important to track how they behave on the website to ensure your site caters effectively to new visitors. 
  • Returning visitors : Returning visitors are coming back to your site for a reason : either they like the content you’re creating or they want to make a purchase. Both instances are great. The more returning visitors, the better. 
  • Bounce rate : This is a measure of how many users leave your website without taking action. Different analytics tools measure this metric differently.
  • Session duration : This is the length of time users spend on your website, and it can be a great gauge of whether they find your site engaging. Especially when combined with the metric below. 
  • Pages per session : This measures how many different pages users visit on average. The more pages they visit and the longer users spend on your website, the more engaging it is. 
  • Traffic source : Traffic can come from a variety of sources (organic, direct, social media, referral, etc.) Tracking which sources generate the most traffic can help you analyse and prioritise your marketing efforts. 
  • User demographics : This broad metric tells you more about who the users are that visit your website, what device they use, what country they come from, etc. While the bulk of your website traffic will come from the countries you target, an influx of new users from other countries can open the door to new opportunities.

  Why do my traffic reports differ ?

  If you use more than one of the methods above to check your website traffic, you’ll quickly realise that every traffic report differs. In some cases, the reasons are obvious. Any tool that estimates your traffic without adding code to your website is just that : an estimate. Tools like Similarweb will never offer the accuracy of analytics platforms like Matomo and Google Analytics. 

  But what about the differences between these analytics platforms themselves ? While each platform has a different way of recording user behaviour, significant differences in website traffic reports between analytics platforms are usually a result of how each platform handles user privacy. 

  A platform like Google Analytics requires users to accept a cookie consent banner to track them. If they accept, great. Google collects all of the data that any other analytics platform does. It may even collect more. If users reject cookie consent banners, however, then Google Analytics can’t track these visitors at all. They simply won’t show up in your traffic reports. 

  That doesn’t happen with all analytics platforms, however. A privacy-focused alternative like Matomo doesn’t require cookie consent banners (apart from in the United Kingdom and Germany) and can therefore continue to track visitors even after they have rejected a cookie consent screen from Google Analytics. This means that virtually all of your website traffic will be tracked regardless of whether users accept a cookie consent banner or not. And it’s why traffic reports in Matomo are often much higher than they are in Google Analytics.

  

  Given that around half (47.32%) of adults in the European Union refuse to allow the use of personal data tracking for advertising purposes and that 95% of people will reject additional cookies when it is easy to do so, this means you could have vastly different traffic reports — and be missing out on a significant amount of user data. 

  If you’re serious about using web analytics to improve your website and optimise your marketing campaigns, then it is essential to use another analytics platform alongside Google Analytics. 

  Get more accurate traffic reports with Matomo

  There are several methods to check website traffic. Some, like Similarweb, can provide estimates on your competitors’ traffic levels. Others, like Google Analytics, are free. But data doesn’t lie. Only privacy-focused analytics solutions like Matomo can provide accurate reports that account for every visitor. 

  Join over one million organisations using Matomo to accurately check their website traffic. Try it for free alongside GA today. No credit card required.